Logistic Regression

• Conditional Logistic Models for Matched Pairs

This presentation is a part of a series of lessons on the Analysis of Categorical Data.  This lecture overs the following: conditional logistic regression, conditional likelihood for matched pairs, the non-central hypergeometric, the conditional maximum likelihood estimator (CMLE), conditional confidence interval for odds ratios, and McNemar's statistic.

• Log-linear Models - Sparse Tables

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: sparse tables, sampling zeros, structural zeros, and log-linear model (and limitations).

• Log-linear Models for Multidimensional Contingency Tables

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: partial/conditional tables, confounding, types of independence (mutual, joint, marginal, and conditional), identifiability constraints, partial odds ratios, hierarchical log-linear model, pairwise interaction log-linear model, conditional independence log-linear model, goodness of fit, and model building.

• Introduction to Log-linear Models

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: conditional independence, log-linear models for 2x2 tables, expected counts, logistic regression, odds ratio, parameters of interest for different designs and the MLEs, poisson log-linear model, double dichotomy, the multinomial, and the multinomial log-linear model.

• Logit Models for Multinomial Responses Part II

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: ordinal regression models, cumulative probabilities, non-proportional odds, score stat for proportionl odds, MLEs, the adjacent categories logit, and proportional odds model.

• Logit Models for Multinomial Responses Part I

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: generalized odds ratio, collapsed categories, polytomous (or multinomial) logistic regression, and maximum likelihood using the multinomial.

• Conditional Logistic Regression

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: unconditional likelihood, elimination of nuisance parameters, and Mantel-Haenzsel estimate.

• Logistic Regression

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: maximum likelihood estimation for logistic regression, sample size requirements for approximate normality of the MLE’s, confidence intervals, likelihood ratio statistic, score test statistic, deviance, Hosmer-Lemeshow goodness-of-fit statistic, the Hosmer-Lemeshow statistic, parameter estimates, scaled/unscaled estimates, residuals, grouped binomials, and model building strategies.

• Logistic Regression: Testing Homogeneity of the Odds Ratio

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: testing for homogeneity of the odds ratio across strata, test statistics for homogeneity (Wald, score, or likelihood ratio statistics), test statistics for homogeneity with ordinal data, logistic regression, and logit for selected sampling.

• Logistic Regression & Common Odds Ratios Part II (with Simulations)

This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Mantel-Haenszel estimator of common odds ratio, confounding in logistic regression, univariate/multivariate analysis, bias vs. variance, and simulations.